Integrated system for generation and retention of medical records

ABSTRACT

A system for populating patient records by use of evidence-based relational database, which compares the medical practitioners diagnoses to predetermined responses, to produce accurate patient chart notes and the integration of stored and generated data into clinical and administrative medical record keeping and billing. Episodic encounters are developed into cases for a specific patient under the care of a practitioner. The subjective symptoms from the patient and the objective observations of the care provider concurrent with the episode are used to form a diagnosis which presents a treatment regimen from an evidence-based relational database and populates medical and administrative templates. Patient history and updated information are retained in the database. “Best practice” treatment plans are continuality placed in the relational database from practice guides and experts in the field.

CROSS-REFERENCE TO RELATED APPLICATION

The present application is a Continuation-in-Part of U.S. applicationSer. No. 10/406,076 filed on Apr. 3, 2003 for Integrated System AndMethod For Documenting And Billing Patient Medical Treatment And MedicalOffice Management said application is herein incorporated by referencein its entirety.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to electronic generation andretention of clinical and administrative medical records; and, moreparticularly, to a system for populating patient records by use ofevidence-based evaluation systems, which compare the medicalpractitioners diagnoses to predetermined objective and subjectiveresponses, including those of experts in the field, to produce accuratepatient chart notes and the integration of stored and generated datainto clinical and administrative medical record keeping and billing.

2. Description of Related Art

Modern medical practices demand accurate medical records of patienthistory and treatment. The information maintained in a patient historyand treatment record is what the physician relies on to carry out acourse of treatment, and also to justify the billing for service.

Thus, medical records exist primarily for the use of health careproviders to record the information related to the continuing care ofeach patient. These records are generally created in two ways. 1.) Byhistorical data entry, usually subjective in nature, of a patient's pastmedical history and health related issues and experiences; and, 2.) byphysicians and medical experts after various episodic encounters with apatient including objective observations, clinical tests, and subjectivesymptom gathering from the patient concurrent with the episode. Usually,as the patient experiences further and differing treatment, theserecords follow the patient from physician to physician. The medicalrecord is episodically updated. From this diverse data the physicianmakes a diagnosis and prescribes a course or regimen of treatment. Theincreasing demands placed on a physician's time, however, results, toooften, in patient charts and records that are improperly maintained anddifficult, at best, to read.

Paper medical records have been used for many years and remain thestandard way of doing things, even today. A patient's medical record isnot a single file, but a multitude of records that are retained amongstmany different providers, hospitals, clinics, and schools. These paperrecords have numerous obvious limitations and drawbacks, including alack of legibility and inconsistency of format. Notes are oftenillegible, paper suffers from wear and can only be in use at onelocation at a time, thus making it unavailable to more than onepractitioner at a time. Further, paper records require large secured(HIPPA) storage spaces as well as numerous shelves or cabinets. A staffof trained personnel must be maintained to manually file, retrieve andkeep track of the records. Loss, damage, or destruction of the recordscan occur due to numerous mishaps such as flood, fire or even a spilledbeverage. Backup of paper records is difficult, time consuming,expensive and many times not current.

Thus, many record keeping systems have become at least partiallyelectronic to alleviate the above problems. While software programsexist to help individually manage the above areas, none of them arecompatible so that one software program will manage an entire medicaloffice. When creating patient history (personal information andmedical/surgical history), for example, patients generally fill outpersonal and medical history information forms. This information is thenentered manually into the patient file and record by office staff intothe office computer system. This is a slow, time consuming, laborintensive process for the office staff/employees.

In order to bill a client, a physician has traditionally completed asuperbill/patient encounter form after a patient's visit. This superbillhas the diagnosis. This (ICD) code and the procedure, (CPT code) whichdescribe the surgery or E&M code details of the encounter is requiredfor billing the patient or insurance company. The office staff thenfills out the insurance claim form (the HCFA 1500 form) manually forbilling the insurance company, or the information and codes are enteredmanually by the office staff into a computer software system which thencreates a patient file. The office staff then can enter the appropriatebilling codes into the insurance claim form (HCFA 1500) which is part ofthe computer system. This can then either be printed out and mailed tothe insurance company or sent electronically to the insurance company.

Even with the advent of automation, dictation remains the primary meansof documenting patient care. The process requires a physician dictatethe patient name, ID, age, and other demographic information, followedby the description of the patient complaints, the observations from theexam, the diagnosis, and description of treatments. This procedure hasto be repeated for each patient encounter to assure accuracy. Because ofthe relationship of symptoms to diagnosis, this entails dictatingrepetitive medical information. Most dictation is hand transcribed intopaper format for the file. Solutions have been applied to improve thedictation efficiency by using automatic speech recognition (ASR) andsuper macros that allow a physician to use a single phrase to describe amedical condition or treatment. ASR has to date been a disappointmentdue to its accuracy problems.

Many software programs exist for assisting the practitioner withbilling. However, the input is still mostly manual. This process is verytime consuming and labor intensive for office staff and expensive to thephysician to pay for the man-hours and labor to perform the tasksrequired for billing. Further, little of this billing software iscompatible with the software for maintaining medical records. Thus, onecannot easily import information from the medical records software toexisting billing software. In addition, there are inventory andrecording problems.

To add to the complexity, when a physician deems necessary, varioussupplies are dispensed to a patient. Some of the supplies that may bedispensed on any given office visit include, but is not limited to,splints, casts, fracture orthoses, pads bandage and dressings, orthoticdevices, and braces. Currently these items etc. are dispensed/given to apatient with virtually no communication to office staff/billingemployees other than recording these on a superbill, which frequentlycan result in missed billing for the supply and failure by the office toreorder and restock the utilized supplies.

Frequently, patients will require prescription medication from aphysician for appropriate treatment of a medical problem. Currently,these are hand written on a paper prescription forms by the doctor andgiven to the patient to take to a pharmacy to be filled and dispensed.This method is slow and labor intensive. Also, errors can occur at thepharmacy due to inability of the pharmacist to read the handwriting ofthe physician resulting in medication and dosing errors for patients.Additionally, some patients lose the paper prescription and consequentlynever obtain necessary medication.

With the ever increasing cost of healthcare, surgeries, medicationsetc., it has become necessary to find means to justify cost and efficacyof medical treatments. Until now, very little can be done to identifyand justify costs and efficacy of treatments. Random studies can be donein teaching hospital settings for studies on procedures. Attempts havebeen made to retrieve data from multiple physician offices to try tostudy effectiveness of various treatments and procedures. Therefore, aneed exists to provide an integrated system and method for documentingand billing patient medical treatment.

Some offices have adopted Electronic Medical Records (EMR) to replacepaper. These systems, although gaining acceptance in the medicalprofession, suffer serious limitations. Some, which are in use, involveelectronic records and templates. One type involves pre-createddocuments that contain specific areas that are quasi-customizable fordocumenting specific patient information. Primary use of these templatesis in administrative record keeping, such as super bills for insuranceinterface and the like.

Further, to facilitate use of EMR, systems have been created to aid inthe process of creating these medical records. Most of the currentapplications, however, are designed to follow the existing paradigm usedfor generating a patient chart note.

These applications use a spread sheet that corresponds to a practicesuperbill to allow the physician to check off boxes that correspond tothe treatment administered. Edit fields are available where thephysician can type in information related to the specifics of thesymptoms observed or to the treatment administered. While useful forgathering and storing information into a database for future retrieval,such systems are cumbersome to use and require a physician to be tied toa computer. Because this is not possible during the patient exam ortreatment, the physician is still left to rely on hand written ordictated notes that are subsequently entered into the spread sheet. Thishas the obvious flaws of causing the possibility of inaccurateinformation and loss of efficiency of the physician's time, as well as,double entry.

Many medical applications software allow the practitioner to slightlymodify the template information or, in some cases, create the templateprior to use. In either case, population of the template involvesdictation or keystroke application of information created by thepractitioner. The ability to customize is often more of a hindrance tothe product's adoption due to the additional burden placed on thepractitioner, the emphasis having been wrongly placed on the ability toadjust the document wording as opposed to its content. While templateshave the advantage of allowing for rapid chart generation, they arelimited by capturing only that information generated by the practitionerto populate the template. In addition, once the template content ismodified, the interactions with other aspects of EMR are modified ordestroyed requiring complete alteration of the system.

Still, other systems have been designed to guide the physician throughthe diagnosis and treatment process. The applications queries thephysician for information, makes suggestions for treatments anddocuments the choices and information typed in. These systems areparticularly cumbersome to use, since a physician having been trained inthe field of medical practice, already knows the diagnosis andtreatments.

In an effort to reduce inconsistency of format the Problem OrientedMedical Record (POMR) was introduced in the 1960s by L. L. Weed. Thissystem relies on the acronym SOAP as a standard approach to recordingentries. The four parts of this acronym are expressed as follows:

Subjective—this summarizes the patient's statement of his or herconcerns, history and the story of what has transpired. It includes thechief complaint or concern.

Objective—the practitioner's observations, and results of physicalevaluation.

Assessment—the practitioner's opinion of diagnosis based on thesubjective and objective findings.

Plan—a course of treatment or plan on what the practitioner intends todo next and instructions to the patient as to treatment and furtherevaluation or testing.

It is well documented within the medical profession that a specific setof symptoms, whether objective or subjective, correlates to a particulardiagnosis. The problem is that the number of symptoms required touniquely identify a particular condition and, therefore, a course oftreatment, is large. Only by use of electronic data handling andinformation flow path analysis is a usable correlation between the twoachievable.

One of the emerging techniques in patient episodic diagnosis is the useof evidence-based practice guide-lines. A problem with this technique isthat professional practitioners are faced with additional data, whichrelates to their profession and impacts their continued ability tomanage professional scenarios. Thus, these professionals, whose job itis to keep up with the latest techniques and information for problemssolving, find themselves in a further information overload. Thissituation translates into disconcertingly low rates of compliance withwidely disseminated evidence-based treatment guidelines even by veryknowledgeable practitioners.

Awareness may not be the only explanation for the modest implementationrate of evidence-based “practice guidelines.” The failure to usefactually based scenarios and evidence-based diagnosis, revolves aroundthe inability of the practitioner to find time to read and digest theoverwhelming volume of data, which relates to their profession andimpacts their continued ability to manage evidence-based professionalscenarios. Thus, medical professionals find themselves in an informationdilemma in an attempt to keep up with the latest techniques andinformation required for diagnosing based upon evidence-based practiceguidelines.

Furthermore, with the passage of HIPPA regulations, EMR systems mustmeet very rigid security standards. Having large databases of patientrelated medical information, without appropriate safeguards is risky.Therefore, a requisite of any large relational database, which storessensitive and complete medical related information associated with aparticular patient, must be secure.

Finally, none of the current medical information systems make anyattempt to pool the treatment data that is produced every day into adatabase that is easily accessible and relational to allow for outcomestudies. Outcome studies have become increasingly desired and demandedby insurance companies to justify payments for patient care. Physicianswould also gain empowerment to validate their practice of medicine.Currently, the National Institute of Health (NIH) is attempting to poolvarious databases created for specific studies into a single database.This plan still fails to address the vast amounts of data produced byprivate practitioners.

It would, therefore, be desirable to have an easy-to-use, accurate,secure system to facilitate the recording of patient treatmentinformation in such a manner as to produce a chart note record, andproduce billing data as well as raw treatment data useful for outcomestudies. It would also be desirable that the system be minimallyintrusive to a physician's manner of work thereby allowing the physicianto maximize time spent with patients. For maximum efficiency, such asystem should also provide means by which personnel, other than thephysician, can interface with the data base to quickly schedulepatients, enter demographic information, and allow patients to directlyenter their medical history either through a kiosk or through anelectronic interchange such as smart cards. Further, it would beadvantageous to have a system, which is computer based, that would selfgenerate much of the information now entered by the physician andcascade this generated information to populate an integratedadministrative and medical record system.

SUMMARY OF THE INVENTION

The present invention provides a system and method for facilitatingpatient treatment, documentation, and billing among heterogeneous users.The system, which is computer based, self generates much of theinformation now entered by the physician and/or staff and cascades thisgenerated information to populate an integrated administrative andmedical record system.

The system employs a common repository for storage of patientdemographics, medical history, treatment history, accounting and billinghistory, and a medical knowledge base that provides a relational database which allows the medical practitioner and/or medical staff toquickly generate medical information based upon a diagnosticdetermination and cascade this generated information to populate anintegrated administrative and medical record system. The system vastlysimplifies the process of documenting patient care and creating detailedchart notes by simple clicks on a user interface.

The inventive system employs an automated tutorial for populatingpatient chart notes, and presents Treatment regimens predicated uponevidence-based “best practice.” These Treatments are advantageouslystatistically predicated upon the strength of the evidence which isinput to the database. In one aspect the system contains independentproblem resolutions and opinions from recognized “experts” in the fieldso that a correlation between the diagnosis and the expected Treatmentoutcome can be rendered. Advantageously, the relational database can becontinually updated as new information and/or practice guidelines becomeavailable, such that interaction of specific scenarios is predicatedupon recommendations in published or otherwise available “practiceguidelines” such that, as decisions are made, this feedback becomesavailable to the user immediately.

The system of the present invention provides a relational database forboth administrative and clinical medical information. Entry of clinicaldata and diagnoses by users having medical knowledge; and, entry ofpractice management information by users having no medical backgroundare provided wherein each user has an interface to the central database.In this manner, the patient based data and information are continuallyupdated.

An automated method for generating medical records including chartnotes, comprising the steps of: diagnosing a patient based uponobjective and/or subjective symptoms and entering the diagnosis into arelational database, which correlates diagnosis to symptoms and relateddata to populate medical records including a patient chart note. Themethod includes population of both subjective and objective information.

The system employs a relational database wherein a Medical KnowledgeBase, patient treatment repository, claims history repository, andpractice management repository are linked to allow medical knowledgedusers, and non-medical knowledged users, using a uniform systemprotocol, to populate patient care documents.

A detailed chart note is generated from the raw treatment data and priorstored medical data based upon the physician's diagnosis. The process ofassembling the chart note entails, mapping of prior patient information,diagnosis, treatments, and findings, as a grouped set with contextsensitivity to a dictionary of descriptors. A language processor ensuresthat the descriptors are assembled in a grammatically correct manner. Areport generator then fills in a report template that corresponds to thediagnosis for the patient visit. Templates are set up for every type ofpatient-provider transaction, such as but not limited to Initial PatientVisit, Follow up Visit, Surgical Consultation, Pre-op H & P, SurgicalReport, Follow up Surgical Report, etc.

The inventive system and method are predicated upon the relationshipbetween a set of symptoms and a specific diagnosis. Specifically, it hasnow been discovered that a relational database can be based upon theprinciple that for every diagnosis there exists a unique subset ofsubjective symptoms (from the set of all symptoms that describe theconditions presented for the diagnosis) such that, upon a physicianrendering a specific diagnosis, the subjective symptoms verbalized by apatient are predictable and can be generated to populate the EMR.Likewise, a unique subset of objective observations (from the set of allobjective observations that uniquely accompany a diagnosis) arepresented such that the medical conditions observed and reported by thepractitioner are clearly understood and predictable and can be generatedto populate the EMR.

In one aspect, wherein multiple diagnoses are identified, the systemcombines the subsets in a manner that accurately reflects the predictedsubjective and objective observations. The merging of subsets ofsubjective and objective observations is handled in one of two manners,as a simple merge or as a “rules based” merge. Where the intersection ofthe set of observations is the null set, simple merging applies. In thiscase, the observations apply to diagnoses that apply to separate anddistinct body systems, e.g. integument and vascular.

Where the intersection of the set of observations is not the null set,then merging is guided by rules that operate on the set ofcharacteristics that are defined by each observation. In practice thishandled by a bit field that defines the observation, whereon each bitcorresponds to a defined characteristic. For example, two diagnosesdescribe an ailment related to skin diseases. One diagnosis describes anobservation of skin tone, the other describes lesions. The observationrelated to the skin tone has a defined characteristic that is adifferent characteristic than lesions and therefore both observationsare allowed. On the other hand, if the two diagnoses define observationsof related to skin tone such that a conflict exists, e.g. one describesorange skin tone, the other pale skin tone, then resolution must behandled by either alerting the practitioner of a possible error indiagnosis, or a tertiary relational rule can be established, whereinunder specific conditions one observation prevails over the other.

Where diagnoses are strongly coupled to specific anatomy, theidentification of an anatomical location, allows definition of subset ofdiagnoses (from the set of all known diagnoses) to further narrow thepossible diagnosis consistent with the anatomically specific subjectivesymptoms and objective observations. This allows the user with a muchmore specific list than the complete list of diagnoses for making thecorrect diagnosis. Advantageously, the system provides a rapid means bywhich the specific location of the pain can be documented and theaffected anatomic tissue identified.

BRIEF DESCRIPTION OF THE DRAWINGS

The following drawings form part of the present specification and areincluded to further demonstrate certain embodiments. These embodimentsmay be better understood by reference to one or more of these drawingsin combination with the detailed description of specific embodimentspresented herein.

FIG. 1 is a relational diagram showing the treatment options inaccordance with the system of the instant invention;

FIG. 2 is a relational diagram using the anatomy aspect in accordancewith the instant invention;

FIG. 3 is an informational flow diagram showing the relation between thediagnoses using the anatomy aspect in accordance with the instantinvention;

FIG. 4 is an information schema showing information flow in accordancewith one aspect of the system of the instant invention;

FIG. 5 is an information flow diagram from physician login to systemreport submission in accordance with one aspect of the instantinvention.

SYSTEM NOMENCLATURE

For the purposes of the present invention, the following terms shallhave the following meanings:

Case: A series of patient episodes (such as appointments) which relateto an initial presenting condition for which an initial (working)Diagnoses was rendered.

Database: An indexed relational data repository, partitioned for eachpractice area, and distributed amongst various Servers.

Diagnosis: The medical term given to describe a specific disease orinjury wherein the subjective description of symptoms and/or theobjective evidence is clearly understood.

Diagnosis ID: The unique identifier of the diagnosis used within thedatabase to identify a specific diagnosis wherein each record in thelookup table is indexed by a unique key.

Electronic Medical Records (EMR): The electronic storage of medicalinformation including diagnosis and treatments for a patient episode ina manner such that the records can be retrieved at any time. For thissystem the record includes a compiled textual chart note, any associatereports, imported images, and treatment data including studies.

Episode: Any transaction between patient and practitioner defined by thelocation of the episode (wherein location as defined by EDI standard),the nature of the transaction (initial consultation, initial treatment,follow up treatment, surgery, etc), and date of service.

Fact: A raw data item specification that defines a range, units ofmeasurement, method of input.

Finding: A fact that is related to a specific diagnosis,diagnosis-treatment, or treatment, that defines a default value withinthe fact range and related Triggers to other findings. Findings arefacts that are defined within the context of a specific diagnosis,diagnosis-treatment, or treatment.

Medicore: A multiple specialty set of database tables as defined bySpecialtycore.

Patient Scheduling and Management: The tasks and rules associated withscheduling patients for office visits subject to the practice managementrules and status for resource availability, and management of patientinvoices, claims, and accounts receivables.

Phrase: A unit of text description, that may include a limited phrase oran entire paragraph and must be narrow in scope so as to relate to aspecific unit of information

Practice: A medical business unit that may encompass one or morePractitioners, of the same or different specialties.

Practice ID is the identifier for the practice and is the unique key tothe practice record in the indexed practices lookup table.(may becoupled with a password for security)

Practice Management: The tasks associated with managing the resources ofa practice, including personnel, supplies, and inventory.

Practitioner: A medical are provider who sees patients with presentingconditions. A Practitioner is identified by Practitioner ID, which is aunique key to the Practitioner record in the indexed Practitionerslookup table.

Raw Treatment Data: The set of data that fully defines all theinformation recorded for a patient's episode, including Diagnoses ID,Treatment IDs, Findings data, dates, and comments.

Server: An application running behind a firewall designed to service anumber of simultaneous requests for service from a multitude of users.

Specialtycore: The set of database tables that define the coreinformation related to a given medical specialty. This includes thefundamental elements or primitives as well as the complex relationships.

Study: Any specialized means of obtaining quantifiable data, forexample, radiology, blood work, biopsy, tissue sample, urinalysis, ekg,sonography, or the like.

Treatment: Any subsequent protocol performed on or by the patient,including procedures, surgery, medication, exam, Study or the like.

Treatment ID: The unique identifier of the treatment that points to thetreatment record within the index treatments lookup table within thedatabase.

Trigger: A relational link which presents secondary information basedupon the particular value of a Fact.

User ID: The unique identifier of a user within the database.

Vocab: The entire set of Macros that relate Diagnoses to Phrases,Treatments to Phrases, and Findings to Phrases

Web App: An application designed to operate over the Internet within acommercial browser.

Web Client App: An application designed to run as an application on alocal client machine that connects to a server via for example a VPN orthe Internet.

DETAILED DESCRIPTION OF THE INVENTION

The present invention is generally a system for facilitating thegathering of patient medical Treatment data useful for generating anaccurate patient chart note, producing a billing claim, and for storingthe raw data in a relational database format that lends itself toproducing outcome studies useful for determining the efficacy of currentand new medical treatments.

The system of the instant invention populates patient records by use ofevidence-based evaluation systems, which compare the medicalpractitioners Diagnoses to predetermined responses, including those ofexperts in the field, to produce accurate patient chart notes and theintegration of stored and generated data into clinical andadministrative medical record keeping and billing.

The system, in one aspect, provides a secure, homogeneous user friendlynetwork for recording medical treatment, practice management, and claimtransactions among diverse users some of whom have medical knowledge andsome that do not using a single communications link between a user and aServer(s).

The system is fully integrated, allowing medical practitioner and staffautomated interface to perform the following tasks: starting a medicalrecord/medical history for a patient file; taking chart notes/creatingmedical records; billing; maintaining inventory/office supplies; andprescriptions; and outcome studies.

The medical practitioner, on the basis of selecting a diagnosis, whichcan be anatomically directed, can generate an expected and/or predictedset of subjective symptoms verbalized by a patient as well as a set ofexpected medical practitioner based observations from stored medicalanalysis retained in a relational data base. Once accepted by themedical practitioner, this informational data is then used toauto-populate medical records. By identifying the location and diagnosisof the complaint the chart notes and reports cascading from there can bepopulated with a minimal of additional input.

It will be realized that the “relational aspect” of the instantinvention is predicated on information flow generated around adiagnoses. The present system operates on the discovered fact that forevery diagnosis there exist a unique subset of symptoms, from the set ofall symptoms that describes the condition that must be present for thecondition to be identified. The present system also operates on asimilar assumption that for every diagnosed condition, there exists aunique subset of objective observations, from the set of all objectiveobservations, that describes the objective observations that must bepresent to identify the condition

Since a Diagnosis is based upon the presenting of observed conditionssupported by the verbalization of various symptoms, when multiplesymptoms or conditions are present, it may be possible that somesymptoms overlap, or it may be possible that additional symptoms mayappear as secondary or even tertiary effects. It is the latter case thatpresents the most difficulty in handling, as the symptom is a result ofan interaction of the ailments and is not a symptom that uniquelysupports the Diagnosis.

As set forth above, the present invention handles this scenario in twoways. First, the set of secondary or tertiary effects is lumped into aset of common symptoms, which can be added to the standard set ofsymptoms extracted for each diagnosis when more than one diagnosis ispresented to the system. Second, an alarm warns the medical practitionerthat two or more non-reconcilable presented symptoms are present and thepossibility of the presence of additional non-verbalized symptoms. Forcommonly grouped conditions these symptoms are well understood and canbe handled automatically. For all other cases the physician is warnedand presented with the list of common interaction symptoms. In thesecases further investigation is indicated before accepting the datapresented by the system.

Object Model—Facts and Findings

To support the detailing of information that is specific for theparticular patient, items such as temperature, size of tumor, angle offracture, lab results, etc, the system defines a data relationship (aFact) that defines the parameters for the information to be collected.Parameters define the units of measurement, range of value, a label foridentification of the data, a default value and preferred means ofinputting the information through the user interface. Not all parametersare necessarily applicable for each Fact. The value of a Fact may pull aTrigger that is a level limit for the Fact such as a blood pressurereading which results in presentment of additional queries and orrelated information.

Another relationship, a Finding, relates a Fact to a given Diagnosis,Treatment, or “diagnosis-treatment” combination, as further describedbelow. For a given Diagnosis, a subset of Findings is defined, from theset of all Findings that includes all the Diagnosis specific Findingsthat must be collected to accurately record the symptoms andobservations to support the Diagnosis. This relates to a specificmedical specialty. Likewise, for a given Treatment, a subset of Findingsis defined, from the set of all Findings that includes all the Treatmentspecific Findings that must be collected to document the Treatment.

An example of the above would be a Diagnosis which results in aprescribed Treatment regimen comprising medication. The Findings wouldinclude the medication name, dose, quantity, sig, etc. A Finding allowsDiagnosis-specific data to be collected for a given Treatment. Forexample, if the Treatment is an X-ray, then the Findings will prompt forfracture information if the Diagnosis is a limb fracture, or percentlung damage if the X-ray is of the chest for a Diagnosis of lung cancer.As can be seen, a matrix is provided such that anatomy based informationor disease based information is used by the system to “interpret”results.

Episodes and Cases

In a further aspect, the relational Database relates a Finding to thedata that is actually input for a specific patient Episode. This EpisodeFinding includes all the Findings that were collected for documentingthe specific patient Case. A Case relationship is defined as that whichrelates the actual occurrence of a specific Diagnosis for a patient. ACase includes the date the patient was diagnosed, the diagnosis, themedical practitioner, and other information. Other relationships, CaseTreatments, Case Findings, Case anatomy, Case supplies define the actualtreatments, findings, affected anatomy, and supplies used, respectivelyfor each patient visit related to a specific Case in progress. AnEpisode journal record is then generated by the system to track the dateof the Episodes in a patient Case to be used for charting the progressof a patient's Treatment.

Database Design—Medical Specialty Fundamental Tables

It will be realized by the skilled artisan that Diagnoses actuateddatabases are based on presenting symptoms and observations which relateto specific medical specialties. Thus, orthopedic surgeons andcardiologists have a differing diagnostic database. The data making upthe core information that defines a medical specialty is referred tohere as the Specialtycore. The group of Specialtycores making up eachspecialty comprises a Medicore data group. The data in eachSpecialtycore can be further grouped into a data set comprisingfundamental components. Examples of some of these data components areanatomy, Diagnosis, Fact, phrase, supply, and Treatment. Databaseidentifiers are established for each component such that it isreferenced as a data grouping or entity. For example, an anatomy groupdefines a name and tissue type; a Diagnosis entity defines a name,billing code, and tissue type; a Fact entity defines a name, range ofvalue, and units; a Phrase defines a tag (a unique name), descriptor,and lexical; a Supply (any medical supply such as syringes, cottonswaps, etc) defines a name and billing code; and a Treatment entitydefines a name, type, and billing code. These examples define theminimum set of fields contained within a group, and it is understoodthat an actual implementation will contain more detail. A set ofentities comprises a first grouping or group “A”.

Medical Specialty Relational Tables

More complex entities are built from the base group A. A second, morecomplex grouping, Group “B” (Entities) relationships, represent the nextlevel of complexity in the hierarchy. These tables define a higher orderof information that establish relationships, as well as, include otheradditional unique information. Examples of group B tables include, butare not limited to, body view, Finding, macro, plan, region, risk, tray,view, and Vocab. It will be realized by the skilled artisan that tablehierarchies can be built in the system, each with a relation to a subsetof the lower tables to add functionality and capability to the system.

For example, a body view relates to a set of anatomy regions visiblefrom a related view; a Finding relates Diagnosis, Treatment, and one ormore Facts which together define a default value; a macro relates toPhrase and defines formatting rules and special circumstances which arediscussed in more detail later; a Plan (the set of treatments selectedby a practitioner to treat a diagnosis) relates a Treatment-set toDiagnosis, and a weighted factor based on “best practice” guidelines; aregion defines a body location and relates to anatomy by defining a setof contents; a tray defines a name, relates to Treatment, and relates tosupply by defining a set of contents; and a Vocab that relates Diagnosisto Phrase, Treatment to Phrase, Finding to Phrase.

Patient and Practice Tables

To record the actual data gathered during an Episode, another group ofdata is defined, called the Practicecore, (the set of tables that isspecific to storing the case specific treatment information, patient andpractitioner demographics, practice information) exists outside theSpecialtycore and relates specific patient information to theSpecialtycore, the most basic element of which is the case. As explainedearlier, a Case includes the date when a diagnosis is made, and containsthe set of records that include all Treatment information for eachEpisode related to the Case. In addition, a case encapsulates adiagnosis-anatomy-temporal set of transactions. An Episode encapsulates,for example, a patient office visit. A case is supported by theadditional sub elements—Case anatomy, Case Findings, Case Treatments,and Case supplies.

A Case anatomy defines the specific region-anatomy, journal recordidentifier, and Case identifier. Flexibility is provided to allow forchanges to the affected anatomy during the course of Treatment. Forexample, for a patient being treated for a skin condition as theaffected region changes, the changes are documented with each visit.Case Findings document the specifics related to the Case that can not beassumed. Some examples of these measurements are temperature, bloodpressure, white blood cell count, size of an ulcer, etc. Case Findingsalso allow for structuring data for use in outcome studies, wheretreatment is correlated to measured changes, or for demographic studiesin which the occurrence of certain diseases is related to geographicregion, patient gender, age, etc. Case Treatments record the treatmentsapplied during each office visit. A Case Treatment is the record oftreatment for the office visit. A Case Treatment relates a journalrecord identifier to a Case identifier and Treatment identifier. The setof Case Treatments for a given Eposode provides the set of codes usedfor producing a billing claim. An Episode journal table contains theinformation that links the patient table, Episode date, and appointmentto the case table. A given Epesode corresponds to a single journalentry.

Root entities in the Specialtycore are defined in a hierarchal order,referred to as parent-child relationships. A child is able to inherit oroverride attributes of the parent in much the same manner as objectoriented programming languages, such as C++. The hierarchy isparticularly powerful for building and maintaining the Specialtycore.

Diagnostic Input

The system employing a database portioned wherein a Medical KnowledgeBase, patient treatment repository, claims history repository, andpractice management repository are linked, allowing users having medicalknowledge and users having no medical background, using a uniform systemprotocol, to route transactions to document patient care.

A Medical Knowledge Base (MKB) comprised of ordered data sets andrelational links provides the core data that is used to assemble amedical chart record. The Medical Knowledge Base is partitioned intospecialty specific databases, hereafter referred to as theSpecialtycore, and practice specific data referred to as thePracticecore. The combined set of Specialtycores that comprise theknowledge base for each specialty makes up the Medicore. The Medicore isthe master database that unifies all the medical specialties, such thatoverlapping knowledge between specialties is not redundantly identified.The system offers the “best practice” for Treating for each Diagnosis byrelating a subset of preferred Treatments from the set of all treatmentsas recommended by the prominent authorities in each specialty.

The Medical Service (the application that implements the means to selecta patient from an appointments list, input anatomical location,diagnosis, and treatments, and generates the patient chart note)component controls the entry and routing of requests from users havingmedical knowledge to the MKB, while the Practice Management componentcontrols the entry and routing of requests from Users with no medicalbackground to the MKB. The system employs a security protocol thatprevents unauthorized users from viewing sensitive patient medicalhistory of treatment data as specified by the HIPAA act. Patients areassigned to a primary care provider within a practice. The primary careprovider controls access to the patient medical records and an assigntemporary access to other providers. Other users with no medicalbackground can be assigned additional privileges in order to completebilling tasks.

Based on the anatomic location, a constrained list of diagnoses isqueried from the database. Advantageously, a user having medicalknowledge selects a diagnosis from the constrained list of diagnosesrelated to the anatomy. A list of related or differential diagnoses isqueried based on the selected diagnosis. The User having medicalknowledge can select any additional diagnoses from this additional list.Where an additional diagnosis is required due to payer requirements, theadditional diagnosis is also indicated with an explanation for theinclusion. If the User having medical knowledge selects any of thedifferential diagnosis, a modifier rule used for billing will triggerwhen the claim is assembled at a later stage.

Other embodiments allow the user having medical knowledge to enter thediagnosis through other means such as through hand writing recognition,voice recognition, or typing. Letter matching can be employed to displaythe constrained list as the user types, or through voice menu prompts.It is understood that the means of data entry can be any means.

Based on the primary diagnosis, a query is made to produce a list oftreatments. A “best practice” Treatment can be indicated as a guide orteaching tool. A past treatment can be indicated if the patient has anytreatment history from a prior visit. From the constrained list oftreatments related to the diagnosis, a treatment is selected. As in thecase for specifying the diagnosis, any means of data entry can beemployed for specifying the treatments. The User having medicalknowledge can also specify a planned treatment for a subsequent visit toreflect the choices discussed with the patient. Special treatments underthe exam category allow the user to enter additional findings that moreaccurately document the patient's specific condition. Exams triggerrules in the billing component to add modifiers that change the way theoffice visit is billed.

A list of required findings is queried based on the chosen anatomy,diagnosis, and treatments. The findings are additional facts that helpto detail information specific to the patient and its diagnosis andtreatment. Facts also include triggers. Triggers are relational rulesfor preset limits on the data range. Fact Triggers based on the findingdata prompt the user for still additional findings, while Macro Triggersbased on the data cause the report description to vary. For example, ahigh temperature reading may alert the user to take a blood pressurereading, and in the report the patient would be indicated as havingfever as opposed to a normal temperature. Findings are entered through avariety of user interface means such as, check list box, radio buttons,slider, drop list box, calendar, dictation box, hand writing control andmore.

Turning to FIG. 1, there is shown a relational diagram between theindicated treatments in accordance with a diagnosis protocol. A careprovider examining a patient renders a Diagnosis in accordance withsubjective verbalized symptoms and objective observations and/or Studydata and selects a Diagnosis as previously described (not shown.) Thesystem based on the diagnosis suggests a “best practice” Treatmentschema comprised of surgery, medication, further examination, furtherStudy, or an additional procedure. The physician then has the option,based upon the “best case” treatments, which are ranked according toevidence based outcomes. The physician then can choose the Treatmentindicated which most closely is aligned with the diagnosis.

FIG. 1, thus, shows the classification of Treatment groups which share acommon characteristic to allow for definition of default behavior whichis common to the class. For example, all prescription Medicationsrequire data for dose, refills, quantity, and SIG; all surgeryautomatically generates a task to remind office personnel to verifypatient insurance before the scheduled surgery; Studies include Labwork,Radiology, ultrasound, etc. Exam includes anatomy system examinations,such as a biomechanical exam, or vitals exam, and evaluations, such as acurrent medication use assessment, allergies assessment. Thus, eachTreatment generates a generic database of related tasks which populatesthe records to accomplish that Treatment.

Thus, Treatment object relationships are built on a parent child basis,wherein the root parent type of Treatment defines behavior for all theTreatment classes. The Procedure module defines a baseline for allTreatments of type Procedure, etc. Parent-Child relationships can thenexist to any level within each Treatment class.

FIG. 2 shows the relationship between the Specialtycore tables Anatomy,Region, and View. Anatomy defines the entire set of anatomy structurespecific to a given medical specialty. View defines the entire set ofperspective views of the body specific to a given medical specialty.Region defines the entire set of regions portioned from the body inevery defined perspective view. The tables Anatomy-Region andRegion-View are relational tables. Anatomy-Region defines the entire setof Anatomies contained in each Region, by relating Anatomy ID to RegionID. Anatomy-Region relates the entire set of regions contained in eachView.

FIG. 3 shows the relationship between the Specialtycore tables Anatomy,Diagnosis, Treatment, and Fact. Diagnosis contains the entire set ofdiagnoses related to the medical specialty. Treatment defines the entireset of treatments for the medical specialty, wherein each Treatment isclassified into one of the Treatment groups as shown in FIG. 1. Factdefines the entire set of raw facts for the medical specialty.

The tables Anatomy-Diagnosis, Plan, and Finding are relational databaselook up tables. Anatomy-Diagnosis defines the entire set of Diagnosesthat are possible for each Anatomy. Additionally, Diagnosis defines thespecific type of anatomy issue to which each Diagnosis relates. Plandefines the regimen of treatments that are useful for treating eachdiagnosed condition in the specialty. In addition, those Treatments thatare the preferred for each Diagnosis are flagged along with thestatistical efficacy defined as a percentage of each Treatment as itrelates to each Diagnosis. Findings define the set of Facts that arerelated to each Diagnosis, Diagnosis-Treatment pair, or Treatment.

FIG. 4 shows the entire flow path of tasks that occur in the process ofpatient Treatment and the relationship of Patient, Physician, and Payer.A Patient makes an appointment for a specific location, has symptoms,receives treatments, submits payment, and has an insurance policy. APhysician (or physicians office) schedules a patient for a specificlocation, makes note of patient's symptoms, performs treatments, updatesthe patient case file, consults the patient case file for medicalhistory information, submits a claim, and receives payment. Payerservices the patient policy, reviews claim information, and makespayment. This system is the framework in which the instant systemoperates.

Turning to FIG. 5, there is shown the work flow path for generating apatient chart file update in accordance with the invention. The workflow of the inventive system and method operates within the informationscenario as shown in FIG. 4 above. A healthcare provider logs on thesystem by use of a Practitioner ID and providing a security credential.He then selects a patient either from an appointment slot or from thelist of patients defined for the practice. Based upon that selection, acase is opened or retrieved depending upon the activity.

A physician then selects the anatomical location corresponding to thelocation of the chief complaint (subjective symptom). From a constrainedlist of possible Diagnoses, generated from the table Anatomy-Diagnosis(shown in FIG. 3), the healthcare provider selects the hypothesized(working) Diagnosis. Then, the healthcare provider enters anyinformation into the set of diagnosis-related Findings, where thosefindings related to subjective and objective observations have defaultvalues that correspond to what should be observed and verbalized for theselected Diagnosis. The set of Treatments administered for the Episodeare then checked off from the list of acceptable Treatments for thegiven Diagnosis. Those Treatments that are defined as the “standard ofcare” are selected by default by the system, but can be changed by thehealthcare provider. Next, a Treatment regimen or plan is presented andagain accepted or modified by the healthcare provider. Where theTreatment is case related, the system default continues previousTreatments. Next, information for the Treatment related Findings isentered. Finally, a timeframe is selected for the next Episode or,alternatively, the physician can schedule the patient directly for thenext appointment.

What is claimed is:
 1. An automated method for generating medicalrecords including chart notes, comprising the steps of: (a) diagnosing apatient based upon objective observations and/or subjective symptoms;(b) entering said diagnosis into a relational database, which correlatesdiagnosis to symptoms to populate medical records including a patientchart.
 2. The method of claim 1 wherein said diagnosis generates atreatment regimen.
 3. The method of claim 2 wherein said treatmentregimen includes best practices.
 4. The method of claim 2 wherein saidtreatment regimen is evidence based.
 5. The method of claim 1 whereinsaid medical records include a super bill.
 6. The method of claim 1wherein said relational database includes statistical evaluation ofefficacy of treatment regimens.
 7. The method of claim 1 wherein saidrelational database includes evidence based best practices treatmentregimens.
 8. The method of claim 1 wherein said relational databaseincludes expert based treatment regimens.
 9. A system for populatingpatient records by use of evidence-based relational database, whichcompares the medical practitioners diagnoses to predetermined responses,to produce accurate patient chart notes and the integration of storedand generated data into clinical and administrative medical recordkeeping and billing.
 10. The system of claim 9 wherein said diagnosesadditionally generates a treatment regimen.
 11. The system of claim 10wherein said treatment regimen includes best practices.
 12. The systemof claim 10 wherein said treatment regimen is evidence based.
 13. Thesystem of claim 9 wherein said relational database comprises a medicalknowledge base, a patient treatment repository, a claims historyrepository, and practice management repository.
 14. The system of claim9 wherein said detailed chart note is generated from the raw treatmentdata and prior stored medical data based upon the physician's diagnosisby mapping prior patient information, diagnosis, treatments, andfindings, as a grouped set with context sensitivity to a dictionary ofdescriptors.
 15. The system of claim 14 further comprising a languageprocessor to ensure that the descriptors are grammatically correct. 16.The system of claim 9 further comprising a report generator to populatea template with information from said relational data base thatcorresponds to the medical practitioner's diagnoses.
 17. The system ofclaim 16 wherein said template is selected from a group consisting ofInitial Patient Visits, Follow up Visits, Surgical Consultations, Pre-opH & Ps, Surgical Reports, Follow up Surgical Reports, and combinationsthereof.
 18. The system of claim 16 wherein said template is selectedfrom a group consisting of a super bill, an invoice, an insurance claimand combinations thereof.
 19. The system of claim 9 further comprisingan anatomy reference view to assist in making the medical practitionersdiagnoses.
 20. The system of claim 9 wherein relational data base isused to assemble outcome studies.